Barbara Pizzileo

Improved Structure Optimization for Fuzzy-Neural Networks

Fuzzy-neural-network-based inference systems are well-known universal approximators which can produce linguistically interpretable results. Unfortunately, their dimensionality can be extremely high due to an excessive number of inputs and rules, which raises the need for overall structure optimization. In the literature, various input selection methods are available, but they are applied separately from rule selection, often without considering the fuzzy structure. This paper proposes an integrated framework to optimize the number of inputs and the number of rules simultaneously. First, a method is developed to select the most significant rules, along with a refinement stage to remove unnecessary correlations. An improved information criterion is then proposed to find an appropriate number of inputs and rules to include in the model, leading to a balanced tradeoff between interpretability and accuracy. Simulation results confirm the efficacy of the proposed method.

A fast method for fuzzy neural network modelling and refinement

In the identification of complex dynamic systems using fuzzy neural networks, one of the main issues is the curse of dimensionality, which makes it difficult to retain a large number of system inputs or to consider a large number of fuzzy sets. Moreover, due to the correlations, not all possible network inputs or regression vectors in the network are necessary and adding them simply increases the model complexity and deteriorates the network generalisation performance. In this paper, the problem is solved by first proposing a fast algorithm for selection of network terms, and then introducing a refinement procedure to tackle the correlation issue. Simulation results show the efficacy of the method.

A New Fast Algorithm for Fuzzy Rule Selection

This paper investigates the selection of fuzzy rules for fuzzy neural networks. The main objective is to effectively and efficiently select the rules and to optimize the associated parameters simultaneously. This is achieved by the proposal of a fast forward rule selection algorithm (FRSA), where the rules are selected one by one and a residual matrix is recursively updated in calculating the contribution of rules. Simulation results show that, the proposed algorithm can achieve faster selection of fuzzy rules in comparison with conventional orthogonal least squares algorithm, and better network performance than the widely used error reduction ratio method (ERR).

A Fast Fuzzy Neural Modelling Method for Nonlinear Dynamic Systems

The identification of nonlinear dynamic systems using fuzzy neural networks is studied. A fast recursive algorithm (FRA) is proposed to select both the fuzzy regressor terms and associated parameters. In comparison with the popular orthogonal least squares (OLS) method, FRA can achieve the fuzzy neural modelling with high accuracy and less computational effort.

Producing Policy-relevant Science by Enhancing Robustness and Model Integration for the Assessment of Global Environmental Change

We use the flexible model coupling technology known as the bespoke framework generator to link established existing modules representing dynamics in the global economy (GEMINI_E3), the energy system (TIAM-WORLD), the global and regional climate system (MAGICC6, PLASIM-ENTS and ClimGEN), the agricultural system, the hydrological system and ecosystems (LPJmL), together in a single integrated assessment modelling (IAM) framework, building on the pre-existing framework of the Community Integrated Assessment System. Next, we demonstrate the application of the framework to produce policy-relevant scientific information. We use it to show that when using carbon price mechanisms to induce a transition from a high-carbon to a low-carbon economy, prices can be minimised if policy action is taken early, if burden sharing regimes are used, and if agriculture is intensified. Some of the coupled models have been made available for use at a secure and user-friendly web portal.

A new algorithm for structure optimization in fuzzy neural networks

Abstract Abstract Reduced complexity in a fuzzy neural network eases the computational burden of construction and training from data, while enhancing the interpretability of the final model. Such structure optimisation can be done either by adjusting the number of inputs and the size of the rule set. In the literature these have generally been addressed independently (Sugeno and Yasukawa [1993], Hong and Harris [2003]). This paper presents a new algorithm where both structural parameters for a fuzzy neural network model are optimized together. Results from simulation examples are given to illustrate the new approach and confirm its advantage over existing methods.

A New Rule Selection Procedure for Fuzzy-Neural Modelling

Abstract In identification of complex dynamic systems using fuzzy neural networks, one of the main issues is the curse-of-dimensionality. In the literature this issue has been addressed by the selection of either the inputs or the rules. Adding unnecessary inputs or rules increases the model complexity and worsens the network generalization performance. Selecting the best set of inputs or rules is a combinational problem and can be computationally expensive. In this paper, the problem is solved by first proposing a refinement procedure for rule selection. The algorithm is then adapted with prior input selection to further improve the model accuracy.

Staged neural modeling with application to prediction of NO x pollutant concentrations in urban air

Addressing the drawbacks of widely used forward neural network growing methods in neural modeling of time series and nonlinear dynamic systems, a staged algorithm is proposed in this paper for modeling and prediction of NOx Pollutant Concentrations in urban air in Belfast, Northern Ireland, using generalized single-layer network. In this algorithm, forward method is used for neural network growing, the resultant network is then refined at the second stage to remove inefficient hidden nodes. Application study confirms the effectiveness of the proposed method.